Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A system for facilitating computer-generated conversations, comprising: a platform comprising a plurality of servers, one or more of the servers configured to: receive via one of a plurality of chat channels a communication from a computing device directed at a chatbot hosted by the platform, the chatbot registered with the plurality of the chat channels and associated with a workflow comprising a plurality of steps, each of the chat channels associated with a communication format different from the communication formats associated with the remaining chat channels; perform an integrity check of the chat channel, the integrity check comprising verifying an identity of the chat channel via which the communication was received; upon the chat channel passing the integrity check, determine using the communication format of the chat channel via which the communication was received one of the steps in the workflow to be taken in response to the received communication; analyze the communication; generate a response communication based on the analysis and the determined step; format the response communication for transmission via the chat channel; and send the response communication via the chat channel to the computing device.
The system facilitates computer-generated conversations by enabling chatbots to interact with users across multiple chat channels, each with distinct communication formats. The system addresses the challenge of maintaining secure and consistent interactions across diverse messaging platforms, such as social media, messaging apps, or enterprise communication tools, while ensuring seamless workflow execution. The system includes a platform with multiple servers, where at least one server is configured to receive communications from a user's computing device directed at a chatbot hosted on the platform. The chatbot is registered with various chat channels and is associated with a predefined workflow comprising multiple steps. Each chat channel has a unique communication format, such as text, voice, or structured data, differing from the others. Upon receiving a communication, the system performs an integrity check to verify the identity of the chat channel, ensuring secure and authenticated interactions. If the channel passes the check, the system determines the appropriate workflow step based on the communication format and analyzes the received message. It then generates a response, formats it according to the channel's requirements, and sends it back to the user's device. This ensures consistent and context-aware interactions across different platforms while maintaining workflow integrity.
2. A system according to claim 1 , one or more of the servers further configured to: register the chatbot with at least one of the plurality of servers, comprising: receive user input comprising one or more of name of the chatbot, an image of the chatbot, the workflow associated with the chatbot, components associated with the chatbot, and the plurality of the chat channels associated with the chatbot, and chat channel configuration preferences; generate the chatbot based on the user input; assign one of the servers comprised in the platform as an endpoint associated with the chatbot at which the communication is received and from which the response communication is sent, wherein the chatbot is one of a plurality of chatbots hosted by the platform and wherein all of the plurality of chatbots are associated with different ones of the servers comprised in the platform as their endpoints; generate credentials for the chatbot, the credentials comprising one or more of an identifier of the chatbot and a password associated with the chatbot; configure the chatbot for the associated chat channels; and store the configured chatbot and the credentials in a storage interfaced to the one or more servers.
A system for managing chatbots within a platform involves registering and configuring chatbots for communication across multiple chat channels. The system includes servers that receive user input specifying details such as the chatbot's name, image, workflow, components, associated chat channels, and configuration preferences. Based on this input, the system generates the chatbot and assigns a specific server within the platform as its endpoint for receiving and sending communications. Each chatbot in the platform is hosted by a distinct server, ensuring dedicated endpoints for all chatbots. The system also generates credentials, including an identifier and password, for authenticating the chatbot. It configures the chatbot for the specified chat channels and stores the configured chatbot and its credentials in a storage system accessible to the servers. This approach enables scalable deployment and management of multiple chatbots, each with unique endpoints and secure authentication, facilitating seamless interaction across various chat channels.
3. A system according to claim 1 , one or more of the servers further configured to: identify an intent of a user associated with the communication, wherein the response communication is generated based on the intent.
This system operates in the domain of automated communication processing, specifically for analyzing and responding to user communications based on detected intent. The problem addressed is the need for systems to accurately interpret user intent in communications and generate appropriate responses without manual intervention. The system includes multiple servers that process communications from users. These servers are configured to analyze the content of a received communication to determine the underlying intent of the user. For example, if a user sends a message requesting information, the system identifies that the intent is informational, and if the user sends a command, the system recognizes that the intent is action-oriented. The system then generates a response communication that aligns with the detected intent. For instance, if the intent is informational, the response may provide the requested data, while if the intent is action-oriented, the response may execute a corresponding action or confirm the request. The system may also include additional servers that handle other functions, such as receiving and routing communications, storing data, or managing user accounts. These servers work together to ensure that communications are processed efficiently and that responses are generated in a timely manner. The overall goal is to enhance the accuracy and relevance of automated responses by dynamically adapting to the user's intent.
4. A system according to claim 3 , one or more of the servers further configured to: obtain a natural language processing of the received communication, wherein the intent is determined based on the natural language processing.
This system relates to communication processing in a networked environment, specifically addressing the challenge of accurately interpreting user intent from natural language inputs. The system includes multiple servers that receive communications from users, such as text or voice messages, and analyze them to determine the underlying intent. The servers are configured to perform natural language processing (NLP) on the received communication to extract meaningful data, including the user's intent. This NLP process involves parsing the communication to identify key elements like keywords, context, and semantic relationships, which are then used to infer the user's purpose or desired action. The system may also include additional servers that handle other tasks, such as routing the communication to the appropriate destination based on the determined intent. The overall goal is to improve the efficiency and accuracy of communication processing by leveraging NLP techniques to automate intent recognition, reducing the need for manual intervention and enhancing user experience. The system is particularly useful in applications like customer service, virtual assistants, and automated support systems where understanding user intent is critical for providing relevant responses.
5. A system according to claim 4 , one or more of the servers further configured to: retrieve from an analytics service data associated with a user associated with the computing device; interact with a third party service based on the natural language processing; and execute the workflow by the chatbot based on the retrieved data and the interaction, wherein the response communication is generated based on the execution.
This invention relates to a system for enhancing chatbot interactions using natural language processing (NLP) and external data integration. The system addresses the challenge of providing context-aware, personalized responses in chatbot interactions by leveraging user-specific data and third-party services. The system includes one or more servers that process natural language input from a user via a computing device. These servers perform natural language processing to interpret the input and generate a response. Additionally, the servers retrieve user-associated data from an analytics service, which may include historical interactions, preferences, or behavioral patterns. The system also interacts with third-party services, such as APIs or external databases, to gather supplementary information relevant to the user's query. Based on the NLP analysis, the retrieved user data, and the third-party service interactions, the system executes a predefined workflow. This workflow determines the chatbot's response, ensuring it is contextually accurate and personalized. The response is then communicated back to the user's computing device. This approach improves chatbot functionality by dynamically integrating real-time data and external services, enhancing the relevance and effectiveness of automated interactions.
6. A system according to claim 5 , wherein the natural language processor is performed by one of at least one of the one or more servers and at least one third party server.
The invention relates to a system for processing natural language data, addressing the challenge of efficiently distributing computational tasks across multiple servers to improve scalability and performance. The system includes a natural language processor that can be executed by either one or more servers within the system or by at least one third-party server. This flexibility allows the system to dynamically allocate processing tasks based on available resources, reducing latency and optimizing resource utilization. The natural language processor is designed to analyze and interpret text data, enabling applications such as chatbots, virtual assistants, or automated content analysis. By leveraging third-party servers, the system can offload processing demands, ensuring consistent performance even during peak usage. The architecture supports seamless integration with external services, enhancing functionality without requiring extensive modifications to the core system. This approach improves efficiency, reduces costs, and ensures reliable natural language processing capabilities across diverse computing environments.
7. A system according to claim 1 , one or more of the servers further configured to: establish an interaction between the computing device and an additional computing device using different ones of the chat channels, comprising: receive a request from the computing device for the interaction involving at least two different ones of the chat channels, the at least two different channels comprising the chat channel via which the communication was received and another one of the chat channels different from the chat channel via which the communication was received; receive an identification of a chatroom created on the chat channel via which the communication was received, wherein the communication was received via the chatroom; receive a request to join the chatroom from an additional computing device via the different chat channel; send the communication received from the computing device to the additional computing device via the different chat channel; receive an additional communication from the additional computing device via the different chat channel; and send the additional communication to the chatroom.
This invention relates to a system for facilitating interactions between computing devices across multiple chat channels. The system addresses the problem of fragmented communication by enabling seamless integration of different chat platforms, allowing users to engage in conversations that span multiple channels while maintaining context and continuity. The system includes servers that manage communication between computing devices using distinct chat channels. When a user initiates an interaction involving at least two different chat channels, the system receives a request from a computing device to establish this interaction. The request includes an identification of a chatroom created on one of the chat channels, where the initial communication was received. The system then receives a request from an additional computing device to join the chatroom via a different chat channel. The initial communication from the first computing device is relayed to the additional computing device through the second chat channel. Subsequently, any responses or additional communications from the additional computing device are sent back to the original chatroom, ensuring that all participants, regardless of their chat channel, remain part of the same conversation. This approach allows users to participate in a unified discussion across disparate platforms without losing context or requiring manual synchronization.
8. A system according to claim 1 , wherein the one or more servers are in a cloud-computing environment and the chat channels are interfaced to the cloud-computing environment.
The system relates to cloud-based communication platforms, specifically addressing the challenge of integrating chat channels with cloud-computing environments to enhance scalability, accessibility, and real-time collaboration. The invention provides a cloud-computing infrastructure that hosts one or more servers, which manage and facilitate communication through chat channels. These chat channels are directly interfaced with the cloud-computing environment, enabling seamless data exchange, low-latency interactions, and centralized management of communication resources. The cloud-based architecture ensures that the system can dynamically scale to accommodate varying user loads, while the integration of chat channels allows for real-time messaging, file sharing, and collaborative workflows across distributed teams. The system may also include features such as user authentication, message encryption, and cross-platform compatibility to enhance security and usability. By leveraging cloud computing, the system eliminates the need for on-premises infrastructure, reducing maintenance costs and improving accessibility for remote users. The invention is particularly useful in enterprise environments where secure, scalable, and efficient communication is critical for productivity.
9. A system according to claim 1 , one or more of the servers further configured to: assign a classification to a user associated with the computing device based on the analysis of the parsed message; execute the workflow based on the classification, wherein the response communication is generated based on the execution.
This system operates in the domain of automated communication processing and response generation, addressing the challenge of efficiently classifying users and generating tailored responses based on their interactions. The system includes multiple servers that analyze and parse messages received from computing devices, such as emails or chat inputs, to extract relevant data. The servers then classify the user associated with the computing device based on the parsed message content. This classification determines the user's intent, preferences, or other attributes, enabling the system to execute a predefined workflow tailored to the classification. The workflow may include steps such as data retrieval, decision-making, or response formulation. The system generates a response communication based on the executed workflow, ensuring the response is contextually appropriate and aligned with the user's classification. This approach enhances automation in customer service, support systems, or other interactive applications by dynamically adapting responses to user-specific characteristics. The system improves efficiency and personalization in automated communication handling.
10. A system according to claim 9 , one or more of the servers further configured to: train the chatbot on a plurality of sample messages, wherein the classification is performed based on the training.
The system relates to chatbot training and message classification in conversational AI. The problem addressed is improving the accuracy and efficiency of chatbot responses by leveraging trained classification models. The system includes servers that train a chatbot using a plurality of sample messages. During training, the chatbot learns to classify incoming messages based on the patterns and structures present in the sample data. This classification enables the chatbot to better understand and respond to user inputs by categorizing them into predefined types or intents. The training process involves analyzing the sample messages to identify relevant features, such as keywords, context, or semantic relationships, which are then used to refine the chatbot's classification algorithm. The trained model allows the chatbot to dynamically adapt to different conversational scenarios, improving its ability to provide accurate and contextually relevant responses. The system ensures that the chatbot's performance is continuously enhanced through iterative training, making it more effective in real-world applications.
11. A method for facilitating computer-generated conversations, comprising: receiving by a platform comprising a plurality of servers via one of a plurality of chat channels a communication from a computing device directed at a chatbot hosted by the platform, the chatbot registered with the plurality of the chat channels and associated with a workflow comprising a plurality of steps, each of the chat channels associated with a communication format different from the communication formats associated with the remaining chat channels; performing by platform an integrity check of the chat channel, the integrity check comprising verifying an identity of the chat channel via which the communication was received; upon the chat channel passing the integrity check, determining by the platform using the communication format of the chat channel via which the communication was received one of the steps in the workflow to be taken in response to the received communication; analyzing by the platform the communication; generating by the chatbot a response communication based on the analysis and the determined step; formatting by the platform the response communication for transmission via the chat channel; and sending by the platform the response communication via the chat channel to the computing device.
This invention relates to a system for managing automated conversations across multiple chat platforms. The problem addressed is the difficulty of maintaining secure, consistent, and context-aware interactions when a chatbot operates across different messaging channels, each with unique communication formats and protocols. The system uses a server-based platform to host a chatbot that can interact with users through various chat channels, such as social media, messaging apps, or enterprise communication tools. Each channel has distinct communication formats, requiring the platform to adapt responses accordingly. When a user sends a message to the chatbot, the platform first verifies the integrity of the chat channel by confirming its identity to prevent unauthorized access. If the verification succeeds, the platform analyzes the message, determines the appropriate step in the chatbot’s predefined workflow, and generates a response based on the analysis and workflow step. The response is then formatted to match the communication standards of the originating chat channel before being sent back to the user. This ensures seamless, secure, and contextually relevant interactions regardless of the messaging platform used. The system supports dynamic workflows, allowing the chatbot to guide users through multi-step processes while maintaining consistency across different channels.
12. A method according to claim 11 , further comprising: registering the chatbot with at least one of the plurality of servers, comprising: receving user input comprising one or more of name of the chatbot, an image of the chatbot, the workflow associated with the chatbot, components associated with the chatbot, and the plurality of the chat channels associated with the chatbot, and chat channel configuration preferences; generating the chatbot based on the user input; assigning one of the servers comprised in the platform as an endpoint associated with the chatbot at which the communication is received and from which the response communication is sent, wherein the chatbot is one of a plurality of chatbots hosted by the platform and wherein all of the plurality of chatbots are associated with different ones of the servers comprised in the platform as their endpoints; generating credentials for the chatbot, the credentials comprising one or more of an identifier of the chatbot and a password associated with the chatbot; configuring the chatbot for the associated chat channels; and storing the configured chatbot and the credentials in a storage interfaced to the one or more servers.
This invention relates to a method for registering and configuring a chatbot within a multi-server platform. The method addresses the challenge of efficiently deploying and managing multiple chatbots across different servers while ensuring secure and scalable communication. The process begins by receiving user input that defines the chatbot, including its name, image, workflow, components, associated chat channels, and configuration preferences. Based on this input, the chatbot is generated and assigned to a specific server within the platform, which acts as its communication endpoint. Each chatbot in the platform is uniquely associated with a different server to distribute the load and optimize performance. The method also generates credentials for the chatbot, such as an identifier and password, to ensure secure access. The chatbot is then configured for its designated chat channels, and both the chatbot and its credentials are stored in a storage system connected to the servers. This approach enables centralized management of multiple chatbots while maintaining scalability and security across the platform.
13. A method according to claim 11 , further comprising: identify an intent of a user associated with the communication, wherein the response communication is generated based on the -intent.
This invention relates to communication systems that analyze and respond to user interactions, particularly in automated or semi-automated environments. The problem addressed is the need for systems to accurately interpret user intent from communications and generate appropriate responses based on that intent, improving the relevance and effectiveness of automated interactions. The method involves processing a communication from a user to determine the underlying intent behind the message. This intent is then used to generate a response communication that aligns with the user's needs or objectives. The system may employ natural language processing, machine learning, or other analytical techniques to extract intent from the communication. The response is dynamically tailored to the identified intent, ensuring that the system's reply is contextually appropriate and useful. The method may also include additional steps such as analyzing the communication for specific keywords, phrases, or patterns that indicate intent, as well as leveraging historical data or user profiles to refine intent detection. The system may further adapt its response strategy based on the user's intent, such as providing detailed information, redirecting to a different service, or escalating the interaction to a human agent if necessary. The goal is to enhance the efficiency and accuracy of automated communication systems, making them more responsive to user needs.
14. A method according to claim 13 , further comprising: obtaining a natural language processing of the parsed message, wherein the intent is determined based on the natural language processing.
The invention relates to message processing systems that analyze and interpret user messages to determine intent. The problem addressed is the need for accurate and efficient intent recognition in messages, particularly in automated systems like chatbots or customer service platforms, where understanding user requests is critical for providing appropriate responses. The method involves parsing a received message to extract relevant components, such as keywords or phrases. This parsed message is then processed using natural language processing (NLP) techniques to analyze its structure, context, and meaning. The NLP processing includes techniques like tokenization, part-of-speech tagging, and semantic analysis to interpret the user's intent. The intent is determined based on the results of this NLP analysis, enabling the system to respond appropriately to the user's request. The method may also involve additional steps, such as validating the parsed message or refining the intent determination through machine learning models trained on historical data. The goal is to improve the accuracy and reliability of intent recognition in automated message processing systems.
15. A method according to claim 14 , further comprising: retrieving by at least one of the one or more servers from an analytics service data associated with a user associated with the computing device; interacting with a third party service based on the natural language processing; and executing the workflow by the chatbot based on the retrieved data and the interaction, wherein the response communication is generated based on the execution.
This invention relates to a method for enhancing chatbot interactions using natural language processing (NLP) and external data integration. The method addresses the problem of limited contextual awareness in chatbots, which often fail to provide personalized or actionable responses due to insufficient data access or integration with external services. The method involves a chatbot system that processes natural language input from a user via a computing device. The chatbot performs NLP to analyze the input and determine an appropriate response. To enhance this response, the system retrieves user-specific data from an analytics service, which may include historical interactions, preferences, or behavioral patterns. Additionally, the chatbot interacts with third-party services, such as APIs or databases, to gather supplementary information relevant to the user's query. The workflow executed by the chatbot is dynamically adjusted based on the retrieved data and the third-party service interactions. This ensures that the response is contextually relevant, personalized, and actionable. For example, if a user requests assistance with a task, the chatbot may pull relevant user history and third-party data to tailor the response, such as suggesting specific tools or steps based on past behavior. By integrating external data and services, the method improves the chatbot's ability to provide accurate, personalized, and efficient responses, addressing the limitations of traditional chatbots that rely solely on predefined scripts or limited datasets.
16. A method according to claim 15 , wherein the natural language processor is performed by one of at least one of the one or more servers and at least one third party server.
The invention relates to natural language processing (NLP) systems designed to analyze and interpret human language data. The core problem addressed is the efficient and accurate processing of natural language inputs, particularly in distributed computing environments. The method involves using a natural language processor to analyze input data, such as text or speech, to extract meaningful information or perform tasks like translation, sentiment analysis, or query response. The processor may be implemented on one or more servers, including third-party servers, to distribute the computational load and improve scalability. This distributed approach allows for flexible deployment, where the NLP tasks can be handled by either the primary system servers or external servers, depending on availability, cost, or performance requirements. The method ensures that the NLP functions remain robust and adaptable across different computing environments, enhancing reliability and efficiency in language processing applications.
17. A method according to claim 11 , further comprising: establishing an interaction between the computing device and an additional computing device using different ones of the chat channels, comprising: receiving a request from the computing device for the interaction involving at least two different ones of the chat channels, the at least two different channels comprising the chat channel via which the communication was received and another one of the chat channels different from the chat channel via which the communication was received; receiving an identification of a chatroom created on the chat channel via which the communication was received, wherein the communication was received via the chatroom; receiving a request to join the chatroom from an additional computing device via the different chat channel; sending by the chatbot the communication received from the computing device to the additional computing device via the different chat channel; receiving an additional communication from the additional computing device via the different chat channel; and sending the additional communication to the chatroom.
This invention relates to a method for facilitating interactions between computing devices using multiple chat channels in a chatbot system. The problem addressed is enabling seamless communication across different chat platforms or channels while maintaining context and continuity in conversations. The method involves a chatbot that receives a communication from a computing device via a specific chat channel, such as a chatroom. The chatbot then establishes an interaction between the original computing device and an additional computing device using different chat channels. This is done by receiving a request from the original device for an interaction involving at least two distinct chat channels, including the original channel and another different channel. The chatbot identifies the chatroom where the initial communication was received and processes a request from the additional computing device to join the chatroom via the different channel. The chatbot then relays the original communication to the additional device through the different channel and forwards any subsequent responses from the additional device back to the original chatroom. This ensures that conversations initiated in one chat channel can be extended or continued across other channels while preserving the context and continuity of the discussion. The method enhances cross-platform collaboration and communication in chatbot-mediated interactions.
18. A method according to claim 11 , wherein the one or more servers are in a cloud-computing environment and the chat channels are interfaced to the cloud-computing environment.
This invention relates to cloud-based communication systems, specifically methods for managing chat channels in a cloud-computing environment. The problem addressed is the need for efficient and scalable communication management in distributed systems, particularly where chat channels must interface with cloud infrastructure to ensure seamless interaction between users and servers. The method involves using one or more servers hosted in a cloud-computing environment to manage chat channels. These servers facilitate communication by interfacing with the cloud environment, allowing chat channels to operate within the cloud infrastructure. The servers handle tasks such as message routing, user authentication, and data synchronization across distributed systems. The cloud-based approach ensures scalability, reliability, and low-latency communication by leveraging cloud resources. Additionally, the method may include features such as real-time message processing, secure data transmission, and integration with other cloud services. The servers dynamically adjust resources based on demand, optimizing performance and cost-efficiency. This ensures that chat channels remain responsive and available even under high traffic loads. The invention improves upon existing systems by providing a more flexible and scalable solution for managing chat communications in cloud environments, reducing reliance on dedicated hardware and improving overall system efficiency.
19. A method according to claim 11 , further comprising: assigning a classification to a user associated with the computing device based on the analysis of the parsed message; executing the workflow based on the classification, wherein the response communication is generated based on the execution.
This invention relates to automated message processing and response systems, particularly for classifying users and generating tailored responses based on their classification. The system analyzes messages sent from a computing device, such as an email or chat message, by parsing the content to extract relevant information. The parsed message is then analyzed to determine characteristics or attributes associated with the user who sent it. Based on this analysis, the user is assigned a classification, which could indicate their role, intent, or other relevant category. The system then executes a predefined workflow corresponding to the user's classification, which generates a response communication tailored to the user's needs or status. This automated classification and workflow execution ensures that responses are contextually appropriate and efficient, improving user interaction and system responsiveness. The method may involve natural language processing, machine learning, or rule-based analysis to parse and classify messages, and the workflows may include actions like routing messages, triggering notifications, or generating automated replies. The system is designed to streamline communication handling in environments like customer support, enterprise messaging, or automated assistants.
20. A method according to claim 19 , further comprising: training the chatbot on a plurality of sample messages, wherein the classification is performed based on the training.
A method for improving chatbot performance involves training a chatbot on a plurality of sample messages to enhance its ability to classify and respond to user inputs. The chatbot is trained using these sample messages to learn patterns and context, enabling it to accurately classify incoming messages based on the training data. This classification process helps the chatbot determine the appropriate response or action for each message, improving its overall effectiveness in handling user interactions. The training process may involve machine learning techniques, such as supervised learning, where the chatbot is exposed to labeled examples of messages and their corresponding classifications. By leveraging this training, the chatbot can better understand user intent and provide more relevant and accurate responses. This method ensures that the chatbot continuously improves its performance by learning from a diverse set of sample messages, making it more reliable and efficient in real-world applications. The training data may include various types of messages, such as customer inquiries, support requests, or general conversational exchanges, to cover a wide range of scenarios the chatbot may encounter. This approach enhances the chatbot's ability to handle different types of interactions and adapt to different user needs.
Unknown
May 5, 2020
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